Summary of the methodology.

Malaria continues to be a severe health problem across the globe, especially within resource-limited areas which lack both skilled diagnostic personnel and diagnostic equipment. This study investigates the use of deep learning diagnosis for malaria through ConvNeXt models that incorporate transfer learning techniques with data augmentation methods for better model performance and transferability. A total number of 606276 thin blood smear images served as the final augmented dataset after the initial 27558 images underwent augmentation. The ConvNeXt Tiny model, version V1 Tiny, achieved an accuracy of 95.9%.; however, the upgraded V2 Tiny Remod version exceeded this benchmark, reaching 98.1% accuracy. The accuracy rate measured 61.4% for Swin Tiny, ResNet18 reached 62.6%, and ResNet50 obtained 81.4%. The combination of label smoothing with the AdamW optimiser produced a model which exhibited strong robustness as well as generalisability. The enhanced ConvNeXt V2 Tiny model combined with data augmentation, transfer learning techniques and explainability frameworks demonstrate a practical solution for malaria diagnosis that achieves high accuracy despite limitations of access to large datasets and microscopy expertise, often observed in resource-limited regions. The findings highlight the potential for real-time diagnostic applications in remote healthcare facilities and the viability of ConvNeXt models in enhancing malaria diagnosis globally.

疟疾仍是全球范围内亟待解决的重大公共卫生难题，尤其在缺乏专业诊断人员与诊断设备的资源匮乏地区更为突出。本研究针对疟疾深度学习诊断展开探索，采用融合迁移学习技术与数据增强方法的ConvNeXt（ConvNeXt）模型，以提升模型性能与可迁移性。初始的27558张血液薄涂片图像经数据增强后，最终构建得到包含606276张图像的增强数据集。其中ConvNeXt Tiny V1模型的准确率达95.9%；而经过升级的ConvNeXt V2 Tiny Remod模型性能更优，准确率达到98.1%。Swin Tiny（Swin Tiny）模型的准确率为61.4%，残差网络18（ResNet18）为62.6%，残差网络50（ResNet50）则达到81.4%。结合标签平滑与AdamW（AdamW）优化器训练得到的模型，具备优异的鲁棒性与泛化能力。本研究中升级后的ConvNeXt V2 Tiny模型结合数据增强、迁移学习技术与可解释性框架，为疟疾诊断提供了一种实用解决方案：即便在资源匮乏地区普遍面临的大型数据集获取困难、显微镜诊断专业技能不足等限制下，仍可实现高精度诊断。研究结果表明，该方案在远程医疗设施中具备实时诊断应用的潜力，同时也验证了ConvNeXt模型在全球范围内提升疟疾诊断效能的可行性。